Python rasa_nlu.config.RasaNLUConfig() Examples
The following are 9
code examples of rasa_nlu.config.RasaNLUConfig().
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Example #1
| Source File: train_online.py From rasa_wechat with Apache License 2.0 | 6 votes |
|
def parse(self, message):
interpreter = Interpreter.load(nlu_model_path, RasaNLUConfig("../mom/nlu_model_config.json"))
intent = interpreter.parse(message)
return intent
# return {
# "text": message,
# "intent": {"name": intent, "confidence": 1.0},
# "entities": []
# }
Example #2
| Source File: train_nlu.py From rasa_wechat with Apache License 2.0 | 5 votes |
|
def train():
training_data = load_data('../mom/data/nlu.json')
trainer = Trainer(RasaNLUConfig("../mom/nlu_model_config.json"))
trainer.train(training_data)
model_directory = trainer.persist('../models') # Returns the directory the model is stored in
return model_directory
Example #3
| Source File: train_nlu.py From rasa_wechat with Apache License 2.0 | 5 votes |
|
def predict(model_directory):
from rasa_nlu.model import Metadata, Interpreter
# where `model_directory points to the folder the model is persisted in
interpreter = Interpreter.load(model_directory, RasaNLUConfig("../mom/nlu_model_config.json"))
print (interpreter.parse("salad"))
# model_directory = train()
# print (model_directory)
Example #4
| Source File: test.py From rasa_wechat with Apache License 2.0 | 5 votes |
|
def parse(self, message):
interpreter = Interpreter.load(nlu_model_path, RasaNLUConfig("../mom/nlu_model_config.json"))
intent = interpreter.parse(message)
return intent
Example #5
| Source File: train_nlu.py From rasa_wechat with Apache License 2.0 | 5 votes |
|
def train_babi_nlu():
training_data = load_data('examples/babi/data/franken_data.json')
trainer = Trainer(RasaNLUConfig("examples/babi/data/config_nlu.json"))
trainer.train(training_data)
model_directory = trainer.persist('examples/babi/models/nlu/',
fixed_model_name=model_name)
return model_directory
Example #6
| Source File: interpreter.py From rasa_wechat with Apache License 2.0 | 5 votes |
|
def parse(self, text):
"""Parses a text message.
Returns a nlu value if the parsing of the text failed."""
if self.lazy_init and self.interpreter is None:
from rasa_nlu.model import Interpreter
from rasa_nlu.config import RasaNLUConfig
self.interpreter = Interpreter.load(self.metadata,
RasaNLUConfig(self.config_file,
os.environ))
return self.interpreter.parse(text)
Example #7
| Source File: yaha_tokenizer.py From Rasa_NLU_Chi with Apache License 2.0 | 5 votes |
|
def train(self, training_data, config, **kwargs):
# type: (TrainingData, RasaNLUConfig, **Any) -> None
if config['language'] != 'zh':
raise Exception("tokenizer_yaha is only used for Chinese. Check your configure json file.")
for example in training_data.training_examples:
example.set("tokens", self.tokenize(example.text))
Example #8
| Source File: rasa_classifier.py From JusticeAI with MIT License | 5 votes |
|
def __init__(self):
self.rasa_config = RasaNLUConfig(self.config_file)
self.trainer = Trainer(self.rasa_config, self.builder)
Example #9
| Source File: rasa.py From bot with MIT License | 5 votes |
|
def __init__(self, data_provider, config_file, data_file, model_dir): logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s') # store unparsed messages, so later we can train bot self.unparsed_messages = [] self.data_provider = data_provider self.data_file = data_file self.model_dir = model_dir self.rasa_config = RasaNLUConfig(config_file)
